图 书 馆
2013年SIGGDD创新奖SIGKDD 2013 Innovation Award |
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| 课程网址: | http://videolectures.net/kdd2013_kleinberg_innovation_award/ |
| 主讲教师: | Jon Kleinberg |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  ACM SIGKDD荣幸地宣布Jon Kleinberg教授获得了2013年创新奖。他在社会和信息网络分析，挖掘网络图，研究网络中的级联行为以及人类行为的算法模型的开发方面做出了开创性的贡献。 ACM SIGKDD创新奖是知识发现和数据挖掘（KDD）领域的卓越技术最高奖项。它授予个人或一组合作者，他们在KDD领域的杰出技术创新对推动该领域的理论和实践产生了持久的影响。 Kleinberg教授为KDD的发展做出了重要贡献。数据挖掘领域。他对新算法的开发以及对新数据挖掘问题的清晰形式化都对该领域产生了深远的影响。他的研究开辟了新途径，开辟了许多新的问题领域。 克莱因伯格可能以其在社交和信息网络方面的工作而闻名。他的工作包括用于计算图中节点的重要度得分的枢纽和权限（HITS）算法，用于预测网络中新链接的出现的方法以及用于通过社交网络最大化影响力传播的算法。克莱因伯格（Kleinberg）的工作对我们对在线网络的结构和演进的了解也产生了深远的影响。例如，他发现网络随着网络直径的缩小而发展，同时随着边缘数量的增长快于节点数量的增长，网络也变得更加致密。此外，Kleinberg还为小世界现象的算法后果做出了根本性贡献。他是第一个认识到斯坦利·米尔格拉姆（Stanley Milgram）著名的“六度分离”实验的人，这不仅暗示着社交网络中个体之间存在短路径，而且人们似乎善于寻找这些路径。该观察结果和随附的模型对社会网络的概念理解以及现代对等系统的设计都具有重要意义。 |
| 课程简介: | ACM SIGKDD is pleased to announce that Prof. Jon Kleinberg is the winner of the 2013 Innovation Award. He is recognized for his seminal contributions to the analysis of social and information networks, mining the web graph, study of cascading behaviors in networks, and the development of algorithmic models of human behavior. ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. Professor Kleinberg has critically contributed to the development of the field of data mining. His development of new algorithms and clean formalizations of novel data mining problems had profound effect on the field. His research has paved new ways and opened many new problem areas. Kleinberg is probably best known for his work on social and information networks. His works include the Hubs and Authorities (HITS) algorithm for computing importance scores of nodes in a graph, methods for predicting the occurrence of new links in networks, and an algorithm for maximizing the spread of influence through a social network. Kleinberg’s work also had profound implications on what we know about the structure and evolution of online networks. For example, he discovered that networks evolve by the network diameter shrinking while also densifying as the number of edges grows faster than the number of nodes. Moreover, Kleinberg also made fundamental contributions to algorithmic consequences of the small world phenomena. He was the first to realize that Stanley Milgram’s famous “six degrees of separation” experiment implied not only that there exist short paths between individuals in social networks but also that people seem to be good at finding those paths. This observation and the accompanying model had implications on conceptual understanding of social networks as well as the design of modern peer-to-peer systems. |
| 关 键 词: | 数据挖掘; 算法模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-16：zyk |
| 最后编审: | 2020-11-16：zyk |
| 阅读次数: | 36 |